The present invention relates to evaluation of data using probabilistic boosting trees.
Discriminative classifiers are often a bottleneck in 3D pose detection routines. Tree-based classifiers, such as Probabilistic Boosting Trees (PBT) and Random Forests, are discriminative models used for vision-based classification and object detection. The classifier is typically evaluated at every pixel in an image, which can be inefficient. The PBT is a general type of decision tree that uses strong classifiers to make fuzzy decisions at internal nodes. Generally, using PBT requires multiple recursive calls, which slows down object detection.
Efficiency can be improved using hierarchical methods or cascades, but 3D medical applications and real-time applications require further efficiency improvements.